ECS Journal of Solid State Science and Technology,
Journal Year:
2024,
Volume and Issue:
13(4), P. 047003 - 047003
Published: April 1, 2024
Plant
leaf
disease
identification
is
a
crucial
aspect
of
modern
agriculture
to
enable
early
detection
and
prevention.
Deep
learning
approaches
have
demonstrated
amazing
results
in
automating
this
procedure.
This
paper
presents
comparative
analysis
various
deep
methods
for
plant
identification,
with
focus
on
convolutional
neural
networks.
The
performance
these
techniques
terms
accuracy,
precision,
recall,
F1-score,
using
diverse
datasets
containing
images
diseased
leaves
from
species
was
examined.
study
highlights
the
strengths
weaknesses
different
approaches,
shedding
light
their
suitability
scenarios.
Additionally,
impact
transfer
learning,
data
augmentation,
sensor
integration
enhancing
accuracy
discussed.
objective
provide
valuable
insights
researchers
practitioners
seeking
harness
potential
agricultural
sector,
ultimately
contributing
more
effective
sustainable
crop
management
practices.
Plant Methods,
Journal Year:
2025,
Volume and Issue:
21(1)
Published: March 11, 2025
Remarkable
inter-class
similarity
and
intra-class
variability
of
tomato
leaf
diseases
seriously
affect
the
accuracy
identification
models.
A
novel
disease
model,
DWTFormer,
based
on
frequency-spatial
feature
fusion,
was
proposed
to
address
this
issue.
Firstly,
a
Bneck-DSM
module
designed
extract
shallow
features,
laying
groundwork
for
deep
extraction.
Then,
dual-branch
mapping
network
(DFMM)
multi-scale
features
from
frequency
spatial
domain
information.
In
branch,
2D
discrete
wavelet
transform
decomposition
effectively
captured
rich
information
in
image,
compensating
convolution
PVT
(Pyramid
Vision
Transformer)-based
developed
global
local
enabling
comprehensive
representation.
Finally,
dual-domain
fusion
model
dynamic
cross-attention
fuse
features.
Experimental
results
dataset
demonstrated
that
DWTFormer
achieved
99.28%
accuracy,
outperforming
most
existing
mainstream
Furthermore,
96.18%
99.89%
accuracies
have
been
obtained
AI
Challenger
2018
PlantVillage
datasets.
In-field
experiments
an
97.22%
average
inference
time
0.028
seconds
real
plant
environments.
This
work
has
reduced
impact
identification.
It
provides
scalable
reference
fast
accurate
Frontiers in Plant Science,
Journal Year:
2025,
Volume and Issue:
16
Published: April 24, 2025
Tomatoes
are
one
of
the
most
economically
significant
crops
worldwide,
with
their
yield
and
quality
heavily
impacted
by
foliar
diseases.
Effective
detection
these
diseases
is
essential
for
enhancing
agricultural
productivity
mitigating
economic
losses.
Current
tomato
leaf
disease
methods,
however,
encounter
challenges
in
extracting
multi-scale
features,
identifying
small
targets,
complex
background
interference.
The
model
Tomato
Focus-Diffusion
Network
(TomaFDNet)
was
proposed
to
solve
above
problems.
utilizes
a
focus-diffusion
network
(MSFDNet)
alongside
an
efficient
parallel
convolutional
module
(EPMSC)
significantly
enhance
extraction
features.
This
combination
particularly
strengthens
model's
capability
detect
targets
amidst
backgrounds.
Experimental
results
show
that
TomaFDNet
reaches
mean
average
precision
(mAP)
83.1%
detecting
Early_blight,
Late_blight,
Leaf_Mold
on
leaves,
outperforming
classical
object
algorithms,
including
Faster
R-CNN
(mAP
=
68.2%)
You
Only
Look
Once
(YOLO)
series
(v5:
mAP
75.5%,
v7:
78.3%,
v8:
78.9%,
v9:
79%,
v10:
77.5%,
v11:
79.2%).
Compared
baseline
YOLOv8
model,
achieves
4.2%
improvement
mAP,
which
statistically
(P
<
0.01).
These
findings
indicate
offers
valid
solution
precise
Sustainability,
Journal Year:
2023,
Volume and Issue:
15(15), P. 11681 - 11681
Published: July 28, 2023
The
growing
global
population
and
accompanying
increase
in
food
demand
has
put
pressure
on
agriculture
to
produce
higher
yields
the
face
of
numerous
challenges,
including
plant
diseases.
Tomato
is
a
widely
cultivated
essential
crop
that
particularly
susceptible
disease,
resulting
significant
economic
losses
hindrances
security.
Recently,
Artificial
Intelligence
(AI)
emerged
as
promising
tool
for
detecting
classifying
tomato
leaf
diseases
with
exceptional
accuracy
efficiency,
empowering
farmers
take
proactive
measures
prevent
damage
production
loss.
AI
algorithms
are
capable
processing
vast
amounts
data
objectively
without
human
bias,
making
them
potent
even
subtle
variations
traditional
techniques
might
miss.
This
paper
provides
comprehensive
overview
most
recent
advancements
disease
classification
using
Machine
Learning
(ML)
Deep
(DL)
techniques,
an
emphasis
how
these
approaches
can
enhance
effectiveness
classification.
Several
ML
DL
models,
convolutional
neural
networks
(CNN),
evaluated
review
highlights
various
features
used
acquisition
well
evaluation
metrics
employed
assess
performance
models.
Moreover,
this
emphasizes
address
limitations
classification,
leading
improved
more
efficient
management
ultimately
contributing
concludes
by
outlining
research
proposing
new
directions
field
AI-assisted
These
insights
will
be
value
researchers
professionals
interested
utilizing
contribute
sustainable
(SDG-3).
Frontiers in Plant Science,
Journal Year:
2023,
Volume and Issue:
14
Published: Oct. 16, 2023
Tomato
leaf
disease
identification
is
difficult
owing
to
the
variety
of
diseases
and
complex
causes,
for
which
method
based
on
convolutional
neural
network
effective.
While
it
challenging
capture
key
features
or
tends
lose
a
large
number
when
extracting
image
by
applying
this
method,
resulting
in
low
accuracy
identification.
Therefore,
ResNet50-DPA
model
proposed
identify
tomato
paper.
Firstly,
an
improved
ResNet50
included
model,
replaces
first
layer
convolution
basic
with
cascaded
atrous
convolution,
facilitating
obtaining
different
scales.
Secondly,
dual-path
attention
(DPA)
mechanism
search
features,
where
stochastic
pooling
employed
eliminate
influence
non-maximum
values,
two
convolutions
one
dimension
are
introduced
replace
MLP
effectively
reducing
damage
information.
In
addition,
quickly
accurately
type
disease,
DPA
module
incorporated
into
residual
obtain
enhanced
feature
map,
helps
reduce
economic
losses.
Finally,
visualization
results
Grad-CAM
presented
show
that
can
more
improve
interpretability
meeting
need
precise
diseases.
In
the
face
of
a
burgeoning
global
population
exceeding
seven
billion
and
dwindling
agricultural
land,
plants
remain
pivotal
for
sustaining
human
civilization's
food
needs.
However,
plant
health
is
threatened
by
various
diseases,
particularly
leaf
ailments
like
spots,
bacterial
infections,
black
spots.
These
afflictions,
predominantly
caused
bacteria
fungi,
jeopardize
crop
yields.
Timely
disease
detection
imperative
safeguarding
productivity.
This
study
introduces
novel
hybrid
approach
amalgamating
MobileNet,
transfer
learning-based
model,
with
SVM
(Support
Vector
Machine)
hinge
loss.
Leveraging
MobileNet's
pre-trained
capabilities,
features
are
extracted
fed
into
an
classifier
to
discern
nine
distinct
types
tomato
diseases
healthy
leaves.
Statistical
analysis
underscores
efficacy
this
surpassing
previous
benchmarks.
Notably,
it
achieves
exceptional
classification
accuracy,
precision,
recall,
AUC
values,
culminating
in
impressive
overall
accuracy
99.37%.